Analyzing Spreadsheets
Analyzes Excel spreadsheets, summarizes trends, and recommends charts when users mention spreadsheets, Excel workbooks, or .xlsx files.
Best use case
Analyzing Spreadsheets is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Analyzes Excel spreadsheets, summarizes trends, and recommends charts when users mention spreadsheets, Excel workbooks, or .xlsx files.
Teams using Analyzing Spreadsheets should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/analyzing-spreadsheets/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How Analyzing Spreadsheets Compares
| Feature / Agent | Analyzing Spreadsheets | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Analyzes Excel spreadsheets, summarizes trends, and recommends charts when users mention spreadsheets, Excel workbooks, or .xlsx files.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Analyzing Spreadsheets
## When to use
- User shares an Excel workbook or asks about spreadsheet analysis
- Tasks include summarizing metrics, spotting anomalies, or drafting charts
- Data lives in tabular form (CSV or XLSX)
## Workflow
1. **Inspect workbook structure**
```python
import pandas as pd
xl = pd.ExcelFile("input.xlsx")
xl.sheet_names
```
2. **Load relevant sheets**
```python
df = pd.read_excel("input.xlsx", sheet_name="Sheet1")
df.head()
```
3. **Clean and validate**
- Drop empty columns/rows
- Normalize date formats with `pd.to_datetime`
- Verify numeric columns with `df.describe()`
4. **Analyze and summarize**
- Use groupby/pivot patterns from [reference/pandas-recipes.md](reference/pandas-recipes.md)
- Highlight KPIs, trends, and outliers
5. **Recommend visuals**
- Suggest chart types (line for time series, bar for categorical comparisons, heatmap for correlations)
- Provide short rationale per recommendation
## Output expectations
- Concise summary (1–3 paragraphs) covering key findings
- Bullet list of insights with supporting numbers
- Optional chart suggestions with column mappings
## Validation checklist
- [ ] Loaded the correct sheet(s) and reported row/column counts
- [ ] Highlighted missing or unusual data
- [ ] Referenced actual values from the workbook
- [ ] Included next-step recommendations (e.g., further slicing, charting)
## Additional resources
- [reference/pandas-recipes.md](reference/pandas-recipes.md) – common aggregation patterns
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